1. Field
The invention relates to substantially continuous self-optimizing method and machine; and more particularly relates to multivariable, real-time self-optimizing method and machine.
2. Prior Art
Present automation systems are extremely useful. With their uses, productivity has greatly been increased. Also, by transferring to machines human intelligence, rather than skill, they have ushered us into this Second Indsutrial Revolution.
No wonder, the industry is rapidly growing; and full of hard-working, reknowned scientists, engineers, and skilled workers diligently laboring day and night. All over the world, new institutions and laboratories are being specially set up to develop new and improved automation technologies, and particularly to provide "artificial intelligence" to these technologies.
But present automation systems are still not smart and must be spoon-fed at every step via computer programs or master-slave instructions. They are also not totally integrated or automated. In addition, they inherit the many defects of the slow, inconsistent, and imperfect human test planners, samplers, testers, data collectors and analyzers, programmers, keypunchers, and machine builders and users. Humans are repeatedly involved but are million times slower and less reliable than microprocessors in, e.g., memory recalling or storing, information inputting or outputting, data analyzing, communicating, and actuating. Most humans can store, recall, compute, or otherwise handle only about three numbers per second and thus have a response time of about 333 ms.
Further, most often these automation systems merely self-organize, self-control, self-adjust, self-adapt, self-regulate, and self-improve; but do not self-optimize, particularly in real time and on many interacting variables.
Yet modern automation systems invariably must deal with fast and complicated processes often involving many unpredictable, interacting, and rapidly changing variables in such categories as: procedures, equipment and facilities, personnel, materials, parts, environment, and/or (international, national, or local) rules or regulations.
My U.S. Pat. Nos. 4,368,509 and 4,472,770 point out that modern technologies generally are complex and fast moving/changing, and require the real-time optimization of many control variables. The one-variable approach is simply inadequate. Even modern control systems that handle one to three variables may not be sufficient. Also, humans are just too slow and unreliable. Hence, multivariate, real-time self-optimizing is often a must. My inventions thus appear to satisfy a long-felt need.
Thus, generally the present automation systems merely passively adapt, adjust, correct, or regulate, in response to variations in only one or a few non-interacting variables or parameters. Dealing with more than a few interacting variables results in extremely large number of tests to be made; and massive amount of data to be collected, conditioned, stored, and instantly analyzed. This is often impractical or impossible, because of the well-known problems of "combinatorial explosion" and "computer intractibility", as will be shown.
Various and voluminous data and information can now be rapidly gathered and interfaced with modern microprocessors, to be handled in real time, but generaly only for transmission, storage, display, or print-out. Usually, the data analyses are untimely completed and not suitable for real-time, multivariable automation procedures.
Artificial intelligence technologies, particularly the expert systems, have thus been developed and increasingly used in various fields. But as shown by the applicant in IEEE Spectrum Forum, Nov. 1983, pp 9-10, the present expert systems are inflexible, costly, qualitative, and often inaccurate or out-of-date. Also, they cannot handle large numbers of interacting variables. More seriously, their response times may be too slow to deal with the many real-world and unpredictable problems of modern automation systems.
Present automation systems also invariably involve many hidden and various errors of sampling and extrapolation of uncertain magnitudes, as shown below.
For example, to optimize a manufacturing system relative to its performances such as productivity, product qualities, production cost and yield, safety, and environmental impact, a control or prediction equation is first developed based on actual or simulation tests on "representative sample manufcturing systems"; and then stepwise programmed onto the microprocessor for execution. The manufacturing system performances depend, of course, on the many variables in the almost always, multi-step manufacturing procedures. The same performances also depend on many other usually neglected but critical variables such as equipment types, ages, operators, and running conditions; materials and parts qualities, vendors, and lots; lubricants types, compositions, and flow rates, directions, or locations; environmental conditions; and the like.
As indicated above, real-time optimization in present automation systems is still limited to handling a few, mostly noninteracting variables. The separate and combined effects of most of the above-listed and many other variables on these systems have never been completely determined or even understood. These effects may also change with time, location, manufacturing system, operators, production runs, . . . In addition, these variables may interact strongly, i.e., have large synergistic or compensating effects. Many of these variables may not even be known or suspected.
With these complexities, the manufacturing system dynamics are typically unpredictable, and often completely or partially unknown or uncertain. It is thus usually impossible to formulate the requisite partial or ordinary differential equations of the system dynamics, or to set up the many associated boundary conditions and equality or inequality constraints, even with many simplifying assumptions and neglecting many or most of the potential variables. The exact or approximate solutions to these intricately coupled equations are even more difficult, if at all possible. For the same reasons, simulations and numerical solutions are often also difficult, incomplete, or impossible.
Classical, closed-loop adaptative control systems based on single response equations each for one or two manufacturing variables may thus not be efficient or even useful.
Still, there remains in each case the real problem of handling a large number of variables in different categories that may or may not be present, important, critical, or interacting. This large number may be 15, 31, 63, 127, or more. All must first be investigated to find out their individual or combined, functional relationships relative to the desired manufacting system performances (such as productivity). Otherwise these performances may not be meaningfully optimized. Merely missing only one or a few of the critical variables may, for example, make the optimization inefficient, irrelevant, misleading, or even dangerous. Yet the very many variables and their surprisingly many interactions, and the vastly more tests normally required have so far made the task of truly optimizing the presently automated, manufacturing (or other) systems hopelessly unmanageable or impossible.
The number of tests n to study completely m variables at only two levels or conditions each is: n=2 to the mth power. For m=15, 31, 63, 127; n=32,768, 2.148.times.10 to the 9th., 9.223.times.10 to the 18th., and 1.701.times.10 to the 38th., respectively.
Taking the last time when m=127, to calculate one batch of the test data only once even on a nonosecond computer would require 5.395.times.10 to the 21st. years for the conventional method. This would exhaust the resources in time, money, personnel, equipment, and samples for all manufacturers or other institutions. To perform the usual data analysis such as regression, correlation, or variance analysis on these test data requires at least about 3.times.n.times.n multiplications. Hence, for m=127, the usual data analyses would require 2.893.times.10 to the 76th. multiplications, or 2.911.times.10 to the 43rd. years on the same nanosecond computer.
This is, of course, an example of the problem of "combinatorial explosion." There is no computer with enough memory or computing speed to perform the data analyses even if these large numbers of tests were made. Hence, the simultaneous problem of "computer intractibility."
Even for a small m, e.g., 3 or 4, the usual practice is to make some "representative sample production runs" within relatively narrow experimental ranges. The experimental design and procedure also leaves much to be desired, i.e., always human-guided or controlled and therefore not only very slow but inconsistent and unreliable. Human reaction times, usually a fraction of a second, even with the best researchers and statisticians, are often orders of magnitude slower than process variations in modern operations. Most humans, for example, can handle only three number per second, and even this noncontinuously. Also, usually the investigator is totally relied on to, but generally cannot, decide which "sample" manufacturing systems to select for testing. How many "sample production runs"? How many tests in each sample run? How to test and measure? What variable combinations on each test run? Even which order to test? . . . The hope and assumption is that these sample runs are truly representative and test results meaningful. Unfortunately, such hope and assumption are sometimes unjustified.
No wonder the test data and the resultant prediction equations used for automation programming often fail to give optimal results. Often, the "optimized" controlled conditions may not be optimal at all. This is particularly true when test results on previously constructed, automation systems are applied to future systems made for different operating conditions with slightly different equipment designs, part vendors or material lots and run by other personnel under changed environments, even to the same, exact specifications, i.e., when various extrapolations exist as is almost always the case.
To compound the dilemma, the fact is that no two automation systems or equipment and, indeed, no two components, or two portions of the same component, on the same system or equipment are identically the same, even though every component is perfectly within specifications. This is partly because of the unavoidably but allowably changing tolerances or clearances on the interacting system components. For example, two matching components may behave very differently even if each component has only one critical dimension at the upper limit of the specification, compared to when the two critical dimensions on both components are at the lower limits, or one at the lower while the other at the upper.
As another example, since no two gear teeths are identically the same in dimensions, materials, and properties, a driving gear with 40 teeth matching a driven gear with 100 teeth actually has 4000 different possible combinations if the driving gear can rotate in only one direction, but has 8000 combinations if the same gear can rotate in both directions. The gear axes may also be differently loaded, bent, or oriented depending on the working conditions and other components such as support bearings and associated gears. Different component combinations such as gear-pinion and gear-belt can similarly combine in innumerable ways. Again the problems of "combinatorial explosion" and "computer intractibility."
The chance combinations of which critical component combinations in the "sample" manufacturing systems are totally unknown and unpredictable. To actually measure the innumerable component dimensions and clearances before or after the assembly of the many components, in different directions and along various lengths under varying, actual loading or operating conditions on the automation systems is again impossible or totally impractical.
More seriously, the initial conditions on dimensions, angles, contact stresses and strains, . . . on the "sample" automation systems are now rendered unknown. Hence, one would still be at a loss even if he had perfectly understood and formulated the requisite system dynamics equations and completely solved these equations.
Thus, modern automation systems in general and automated manufacturing systems in particular must be highly flexible, independent and intelligent; and capable of real-time self-optimizing on many variables. These systems must also be very efficient in data handling and information extracting. Also, the only meaningful way to truly optimize the performances of a particular object including manufacturing system should include:
(1) minimizing the chance variations through replicated tests and statistical averaging; and
(2) determining the unique functional relationships between the many relevant variables and the performances of this very object or automation system during the particular operation of the object itself.
Yet because of a potentially large numbers of interacting variables, present automation systems cannot even perform a single, complete round of testing. Statistical averaging is thus impossible and chance variations can often be critical.
True and vigorous optimization also requires that the instantaneous optimum combinations of all the relevant variables be quantitatively computed, continuously. In addition, these many variables must be set at these unique optimum combinations at the very instants the functional relationships and optimum variable combinations are determined and before these relationships and combintions change.
These tests, determinations, and variable settings must thus be done dynamically, often very rapidly at very high speeds, to be periodically checked and adjusted every minute, second, or fractional second as is needed. That is, the optimizing cycles or response times must be extremely short and human interactions must be absent.
Hence, efficient, real-time self-optimizing, while absolutely necessary for modern automation systems, is still not available. Human guided, controlled, supervised, or interacted systems are far too slow and unreliable. One also simply cannot rely on chances, hopes, and assumptions.
Accordingly, an object of the present invention is to provide improved self-optimizing machine and method;
A further object of the invention is to provide real-time, self-optimizing machine or method capable of handling tens, hundreds, thousands, or more variables with minimal human guidance;
Another object of this invention is to provide self-optimizing machine or method which can be optimized practically continuously and instantly;
A broad object of the invention is to provide self-optimizing machine or method based on totally self-planned, controlled tests performed on the very particular machine or method itself without relying on extrapolation from samples test results obtained on other similar machines or systems;
Another object of the invention is to optimize machine or method by the installation thereon batteries of modern microelectronics, sensors, actuators, signal-transmission lines, and related devices;
A further object of the invention is to provide self-optimizing machine or method which actively seek, and automatically set at, the instantaneous optimum combinations of the many relavant variables in various categories, with instant feed-back on the status of optimization to supply data for immediate redesigning, retesting, and reoptimizing, all without human intervention.
Further objects and advantages of my invention will appear as the specification proceeds.